Road transport faces significant challenges, including safety issues, environmental impacts, and inefficient driving behaviours, which are often analysed in isolation. This doctoral thesis primarily aims to develop and validate an integrated Driving Indicator (iDI), to integrate it into driver assistance systems, and address existing gaps through the definition of driving profiles based on Real Driving Emissions (RDE) data. Additionally, a classification of driving styles was proposed to assess the results obtained. Both the indicator and the classification were statistically validated, confirming their reliability. The research was based on a comprehensive methodology, starting with the collection of empirical data, including Portable Emissions Measurement Systems (PEMS), to assess the feasibility of distinguishing and classifying driving profiles. Statistical tools such as biplots and machine learning algorithms like Random Forest were applied. A second-by-second analysis of vehicle engine and emissions data revealed consistent patterns between driving behaviour and pollutant emissions, with classification accuracy reaching 99%. The variation of acceleration (jerk) was also found to significantly influence CO2 and NOx emissions across all the driving styles and route types. The iDI development involved solving an optimisation problem (using Matlab), conducting traffic simulations in VISSIM, and performing conflict analysis with the Surrogate Safety Assessment Model (SSAM). The indicator integrates safety metrics (headway time, stopping distance), volatility (acceleration, jerk), and operational performance or emissions (CO2, NOx), allowing classification into four driving behaviours: calm, normal, aggressive, and very aggressive. In the simulations, light-duty vehicles represented 72% of the fleet but accounted for 96% of traffic incidents, showing iDI scores between 20% and 30% (very aggressive). According to SSAM analysis, drivers identified as “very aggressive” by the iDI and the proposed classification were responsible for more traffic conflicts. Empirical validation extended to coexistence zones and roundabouts with crosswalks, where measurements in real-world conditions (using PEMS) showed CO2 emissions 49% above the manufacturer’s reference and NOx levels exceeding the Euro 6 limit by 340%. This research makes several methodological contributions: the pioneering use of biplots with empirical data in transport studies, the creation of a multidimensional indicator synthesising various aspects of driving behaviour, and comprehensive validation processes. The findings support the development of public policy, as the iDI effectively integrates safety, environmental, and operational factors with the potential to provide real-time information to drivers.

Driver behaviour contributes significantly to road safety and environmental impacts. Therefore, a better understanding of how drivers react to different stimuli, such as road events and vehicle specifications, is fundamental in trying to minimise these adverse effects. Furthermore, an often-neglected component in the reference literature is the concept of comfort experienced by the driver and how it impacts the driving profile. Considering the global concerns of improving air quality, reducing emissions and increasing road safety, it is important to study and evaluate approaches to improve comfort and the driving/travelling experience. A useful tool in this area is the driving simulator, as it allows you to control the environment and scenarios based on real conditions without having the consequent risks of road and environmental conflicts that a test in a real environment would have. The main objective of this dissertation is to identify parameters related to driving behaviour that can contribute to the development of an indicator of comfort and travel experience. To achieve this goal, this research uses data collected from experiments conducted in a fixed driving simulator, in an urban scenario, involving different singularities. In this study, 30 volunteers underwent six simulations (three using an automatic transmission vehicle and three using a manual transmission vehicle) in an urban environment to obtain a significant sample considering the individual variability of each participant's driving behaviour. Throughout this simulation, several variables were collected on a second-by-second basis, such as speed, acceleration, and distance to the intersection, among others. From these variables, it was also possible to calculate driving volatility, which consists of the acceleration variation over time or jerk, the vehicle's specific power (VSP), and the VSP mode, which, in turn, provides the emission model used in this dissertation. Emission factors obtained from previous studies were used to estimate the instantaneous emissions resulting from the experiments. The simulator variables and the calculated ones were subsequently examined both in a general context and concerning specific road characteristics (e.g., a segment involving a speed bump and crosswalk, an intersection with a stop sign, an intersection with a yield sign, an intersection with a crosswalk, and finally an intersection with a traffic light). Specifically, a microscopic analysis was conducted along the area of influence of these events. As a result of this investigation, patterns were identified, and deviations were detected that allow the characterisation of driving behaviour in the studied events highlighting specific aspects that could be associated with assessing comfort. The overall results of the simulations allow to draw conclusions regarding the transmission modes, indicating a greater tendency for higher speeds and acceleration profiles in vehicles with automatic transmission compared to manual ones. In the study of the events, it was observed that for situations where the driver did not need to fully stop the vehicle had a lesser environmental impact, as seen in the case of yielding. On the other hand, events that may lead the driver to stop, such as traffic lights, pedestrian crossings, and mandatory stop intersections, cause jerk levels to increase, creating a greater sense of discomfort for the driver. In summary, we believe that the results obtained will be relevant to support decision- making regarding individual measures (e.g., transmission change indicators or controllers with sustainability objectives to minimise emissions and enhance comfort and safety) and policies (e.g., urban infrastructure planning strategies) to improve road safety as well as mitigate the environmental impacts intrinsic to driving tasks.

The presence of new technologies inside vehicles is becoming more common. Due to this fact, the potential changes produced on the driving task and also on the road safety must be examined. With the intention of contributing to amplify this knowledge, the present research aimed to study the impact of multiple visual and auditory inputs from in-vehicle information systems on the driver behaviour. It was investigated the interaction with more than one invehicle system (a guidance system and a mobile phone device) and verified its consequences on the drivers’ activity. To accomplish this goal two experimental moments were planned: one conducted in a real context and another in a simulated environment. Results revealed that the interaction with two in-vehicle systems produced considerable changes on drivers’behaviour once subjects assumed more frequently unsafe actions like: inadequate indication of their actions; abrupt and unexpected adoption of determined behaviours; and also negligence of some road information from the environment. It was also verified that this situation produced more severe consequences to the driving task performance of elderly drivers. The management of all sources of information induced them to compromise their safety and be more frequently involved in dangerous situations.